Method and system for predicting failure of mining machine crowd system

ABSTRACT

Methods for monitoring a machine are described. In one aspect, a method includes receiving information on a plurality of events associated with the machine, determining a severity value for at least one event of the plurality of events, the severity value based on at least one of a safety value, a hierarchy value, a time-to-repair value, and a cost-of-repair value, and outputting an alert includes the severity value if the severity value exceeds a predetermined threshold associated with the at least one event. Systems and machine-readable media are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a divisional of co-pending U.S. patentapplication Ser. No. 15/946,291, filed on Apr. 5, 2018, which is adivisional of U.S. patent application Ser. No. 13/106,574, now U.S. Pat.No. 9,971,346, filed on May 12, 2011, which claims priority to U.S.Provisional Patent Application No. 61/334,657, filed on May 14, 2010,the entire contents of all which are hereby incorporated by reference.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

None

TECHNICAL FIELD

The present disclosure generally relates to equipment monitoring, andspecifically, to remotely monitoring heavy duty machinery.

DESCRIPTION OF THE RELATED ART

It is well known that heavy duty industrial machinery requiresmaintenance to maintain machine uptime. As machines increase in size,complexity, and cost, failure to maintain the machines results ingreater impact to production and cost. Information on why a machinefailed is often not captured, thereby making it difficult to identifyand troubleshoot any problems that led to the failure. Furthermore, evenif the information is captured, it is usually stored onboard themachine, making it inaccessible to remote maintenance staff, therebyhindering root cause analysis and condition-based maintenanceinitiatives. Thus, while machine maintenance systems according to theprior art provide a number of advantageous features, they neverthelesshave certain limitations.

The present invention seeks to overcome certain of these limitations andother drawbacks of the prior art, and to provide new features notheretofore available. A full discussion of the features and advantagesof the present invention is deferred to the following detaileddescription, which proceeds with reference to the accompanying drawings.

SUMMARY

What is needed is a system for capturing information related to machineproblems that allows the information to be accessible to remotemaintenance staff. What is also needed is the ability to provide usersof the machine with real-time information, data, trending and analysistools to rapidly identify a cause of a machine problem in order toreduce unplanned downtime. What is further needed is the ability toprovide remote maintenance staff access to the machine in order to solvethe machine problem remotely, thereby reducing downtime associated withdiagnosing faults.

In certain embodiments, the disclosed systems and methods increase theefficiency and operability of a machine by remotely collecting andanalyzing machine data, and then predicting events and faults beforethey occur in order to prevent failures. The data is further reviewed toidentify issues that require attention, allowing for streamlinedanalysis and workflow processes. The information is used to moreaccurately predict the actual time of planned maintenances, reduceunnecessary maintenances, and increase machine availability. Theinformation is also used to identify design improvement opportunities toincrease the machine's performance and quality. The information, whichincludes machine health and performance data, can further be used toavert machine breakdowns, target and predict maintenance actions, andimprove machine uptime and cost per unit. The information facilitatesimproved surveillance of the machine, accelerates response tobreakdowns, reduces the need for unscheduled maintenance, helps improveoperating practices, proactively detects failures in time to preventcascade damage, captures expertise of qualified personnel, provides realtime feedback to enhance operator skill and performance, and enablesbest practices and significantly extends machine life that may reducemean time to repair (MTTR), increase uptime, reduce operations costs,reduce maintenance costs, reduce warranty claims, improve mean timebetween failure (MTBF), improve mean time to shutdown (MTTS), improveproductivity, improve utilization, improve responsiveness to faults, andimprove parts lead time.

In certain embodiments, a method for monitoring a machine is disclosed.The method includes receiving information on a plurality of eventsassociated with the machine, determining a severity value for at leastone event of the plurality of events, the severity value based on atleast one of a safety value, a hierarchy value, a time-to-repair value,and a cost-of-repair value, and outputting an alert includes theseverity value if the severity value exceeds a predetermined thresholdassociated with the at least one event.

In certain embodiments, a system for monitoring a machine is disclosed.The system includes a memory including information on a plurality ofevents associated with the machine, and a processor. The processor isconfigured to determine a severity value for at least one event of theplurality of events, the severity value based on at least one of asafety value, a hierarchy value, a time-to-repair value, and acost-of-repair value, and an output module configured to output an alertincludes the severity value if the severity value exceeds apredetermined threshold associated with the at least one event.

In certain embodiments, a machine-readable storage medium includesmachine-readable instructions for causing a processor to execute amethod for monitoring a machine is disclosed. The method includesreceiving information on a plurality of events associated with themachine, determining a severity value for at least one event of theplurality of events, the severity value based on at least one of asafety value, a hierarchy value, a time-to-repair value, and acost-of-repair value, and outputting an alert includes the severityvalue if the severity value exceeds a predetermined threshold associatedwith the at least one event.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide furtherunderstanding and are incorporated in and constitute a part of thisspecification, illustrate disclosed embodiments and together with thedescription serve to explain the principles of the disclosedembodiments. In the drawings:

FIG. 1A illustrates an architecture that includes a system for remotelymonitoring equipment in accordance with certain embodiments.

FIG. 1B illustrates separate communications channels for transmittinginformation between the equipment client and the server system of FIG.1A in accordance with certain embodiments.

FIG. 2 is an exemplary screenshot from a web client displaying adashboard of information regarding a fleet of machines being monitoredby the system of FIG. 1A.

FIG. 3A is an exemplary state diagram of basic machine states for amachine being monitored by the system of FIG. 1A.

FIG. 3B is an exemplary state diagram of basic run states for a machinebeing monitored by the system of FIG. 1A.

FIG. 4 is an exemplary screenshot illustrating a runtime distributionchart for a machine being monitored by the system of FIG. 1A.

FIG. 5 is an exemplary screenshot illustrating productivity and otherinformation for a machine being monitored by the system of FIG. 1A.

FIG. 6 is an exemplary screenshot illustrating load distribution for amachine being monitored by the system of FIG. 1A.

FIG. 7 is an exemplary screenshot illustrating information on outagesfor a machine being monitored by the system of FIG. 1A.

FIG. 8 is an exemplary screenshot illustrating cycle time performanceinformation for a machine being monitored by the system of FIG. 1A.

FIG. 9 is an exemplary screenshot illustrating availability history fora fleet of machines being monitored by the system of FIG. 1A.

FIG. 10 is an exemplary screenshot illustrating time between shutdownsfor a fleet of machines being monitored by the system of FIG. 1A.

FIG. 11 is an exemplary screenshot illustrating a short term trendrepresenting incoming voltage to a machine being monitored by the systemof FIG. 1A.

FIG. 12 is an exemplary screenshot illustrating a long term trendrepresenting incoming voltage to a machine being monitored by the systemof FIG. 1A.

FIG. 13 is an exemplary mobile device displaying information formattedfor a mobile device using the system of FIG. 1A.

FIG. 14 is an exemplary screenshot illustrating alarm information for afleet of machines being monitored by the system of FIG. 1A.

FIG. 15 is an exemplary screenshot illustrating in-depth fault analysisfor a fleet of machines being monitored by the system of FIG. 1A.

FIG. 16 is an exemplary screenshot illustrating a historic analysis oftemperatures for a fleet of machines being monitored by the system ofFIG. 1A.

FIG. 17 illustrates an exemplary screenshot of a normal trend betweentwo bearings on a hoist drum of a machine being monitored by the systemof FIG. 1A.

FIG. 18A illustrates an exemplary screenshot for configuring alertcommunications using the system of FIG. 1A.

FIG. 18B illustrates an exemplary screenshot for viewing a history ofalerts communicated by the system of FIG. 1A.

FIG. 18C illustrates an exemplary screenshot for an alert communicationcommunicated by the system of FIG. 1A.

FIG. 19A illustrates an exemplary screenshot of a list of faults for amachine being monitored by the system of FIG. 1A.

FIG. 19B illustrates exemplary weighting determinations for variousfaults identified by the system of FIG. 1A.

FIG. 19C illustrates an episode of events for a machine being monitoredby the system of FIG. 1A.

FIG. 19D illustrates an exemplary report output by the system of FIG.1A.

FIG. 20A illustrates a comparison of exemplary workflows between thesystem of FIG. 1A and the prior art.

FIG. 20B illustrates an exemplary workflow for predicting a machineevent using the system of FIG. 1A.

FIG. 21A is an exemplary screenshot identifying crowd field oscillationon a machine being monitored by the system of FIG. 1A and FIG. 21B moreclearly illustrates the crowd field oscillation.

FIG. 22 is a block diagram illustrating an example of a computer systemwith which the system of FIG. 1A can be implemented.

DETAILED DESCRIPTION

While this invention is susceptible of embodiments in many differentforms, there is shown in the drawings and will herein be described indetail preferred embodiments of the invention with the understandingthat the present disclosure is to be considered as an exemplification ofthe principles of the invention and is not intended to limit the broadaspect of the invention to the embodiments illustrated. Additionally, inthe following detailed description, numerous specific details are setforth to provide a full understanding of the present disclosure. It willbe obvious, however, to one ordinarily skilled in the art that theembodiments of the present disclosure may be practiced without some ofthese specific details. In other instances, well-known structures andtechniques have not been shown in detail not to obscure the disclosure.

Referring now to the Figures, and specifically to FIG. 1, there is shownan architecture 10 that includes a system 10 for remotely monitoring amachine 128 in accordance with certain embodiments. The architectureincludes a server system 100, equipment client 110, and web client 124,connected over a network 122.

The server system 100 is configured to remotely monitor machines 128,such as, for example, drills, conveyors, draglines, shovels, surface andunderground mining machines, haulage vehicles, mining crushers, andother heavy machinery which include an equipment client 110. The system100 includes a communications module 102, a processor 104, and a memory106 that includes a monitoring module 108. The server system 100 can belocated at a facility remote to the machine 128 (or “equipment”), suchas in a remote office building. In certain embodiments, the serversystem 100 includes multiple servers, such as a server to storehistorical data, a server responsible for processing alerts, and aserver to store any appropriate databases.

The system processor 104 is configured to execute instructions. Theinstructions can be physically coded into the processor 104 (“hardcoded”), received from software, such as the monitoring module 108, or acombination of both. In certain embodiments, the monitoring moduleprovides a dashboard accessible by the web client 124, and instructs thesystem processor 104 in conducting an analysis of the data 118 receivedfrom the equipment client 110. The monitoring module 108 may alsoprovide a workflow based on data 118 (or “data log 118”) received fromthe equipment client 110. As discussed herein, data 118 is collected atthe machine 128 by the equipment client 110 using sensors (a termunderstood to include, without limitation, hydraulic, electronic,electro-mechanical or mechanical sensors, transducers, detectors orother measuring or data acquisition apparatus) appropriately placed inand around the machine 128. The sensors (not shown in the figures),which can obtain, for example, temperature, voltage, time, and a varietyof other forms of information, are coupled to the equipment client 110via appropriate means. The data 118, once collected by the sensors, canbe logged in memory 116 that is typically located on or near theequipment client 110. As discussed in more detail below, the data 118can be subsequently transmitted or otherwise provided to the memory 106of the server system 100 over the network 122 or by other means. Theworkflow and related tools allow for the rapid transfer of informationbetween the equipment and a workforce that reduces a mean time to repair(MTTR) and unplanned downtime. The workflow tools further allow a userto create, modify, and delete alerts, provide resolution input (e.g.,action taken, comments), and track and/or monitor workflow. Theconducted analysis includes root cause analysis and critical issueidentification focusing on rapid detection resulting in less downtimefor problem resolution.

In one embodiment, the system processor 104 is configured to process andoptionally store information from the equipment client 110, such as, butnot limited to, episodes, runtime, abuse factors, electrical downtime,cycle information, payload information, loading efficiency, machinehours, tonnage summary, cycle decomposition, availability, voltage,runtime (e.g., total, hoist, crowd, propel, etc.), raw criticalequipment parameters, measurements, and status(es). For example, forshovels, the abuse factor can be calculated based on swing impacts, boomjacks, operating hours, payload overloads, motor stalls, andundervoltage events. The server system 100 is configured to provideremote, reliable, and accurate information and analysis tools for themachine 128 to optimize the health and performance of the machine 128.

Exemplary computing systems 100 include laptop computers, desktopcomputers, tablet computers, servers, clients, thin clients, personaldigital assistants (PDA), portable computing devices, mobile intelligentdevices (MID) (e.g., a smartphone), software as a service (SAAS), orsuitable devices with an appropriate processor 104 and memory 106capable of executing the instructions and functions discussed herein.The server system 100 can be stationary or mobile. In certainembodiments, the server system 100 is wired or wirelessly connected to anetwork 122 via a communications module 102 via a modem connection, alocal-area network (LAN) connection including the Ethernet, or abroadband wide-area network (WAN) connection, such as a digitalsubscriber line (DSL), cable, T1, T3, fiber optic, cellular connection,or satellite connection. In the illustrated embodiment, the network 122is the Internet, although in certain embodiments, the network 122 can bea LAN network or a corporate WAN network. The network 122 may includefeatures such as a firewall.

The equipment client 110 is configured to transmit to and receiveinformation from server system 100 over network 122, such astransmitting data 118 (e.g., a data log) of the equipment and receivingcontrol commands for the equipment. In certain embodiments, theequipment client 110 is located within the machine 128, such as within asecure compartment of an electric shovel. The equipment client 110includes a communications module 112, a processor 114, and a memory 116that includes a control module 120 and the data 118.

In certain embodiments, the data 118 can be stored and transmittedlater. The later transmission can be, for example, every few seconds,every minute, for longer periods, or after a certain time limit or datasize limit is reached. The ability to transmit the data 118 in periodsaddresses the risk of a network 122 failure while also allowing the data118 to be current data for the machine 128. The ability to transmit thedata 118 in periods also allows the data 118 to be batched before beingtransmitted.

The equipment client processor 114 is also configured to store, inmemory 116 data 118 related to the machine 128. At least two types ofdata 118 are stored, trend data and event data. Trend data generally istime series data of a particular measurement, such as temperature orvoltage. Event data generally are warnings, faults and state messagescoming from or generated by the equipment, which assists in providinginformation on the cycle decomposition for the machine 128, as discussedin more detail below. Any trend data or event data stored in the data118 can be transmitted to the server system 100 for display at the webclient 124. In certain embodiments, the transmitted data 118 comprisesthe trend data and the event data. As illustrated in FIG. 1B, the trenddata can be transmitted on a first channel 113 a (e.g., a virtualchannel having a virtual channel identifier), over the network 122,separate from a second channel 113 b transmitting the event data. Thecycle decomposition state machine, which is executed on board themachine 128 as it is operated, identifies each event. Specifically, amachine state event is created for each state transition as raw data isanalyzed by the equipment client 110 in real time. The states are thentransmitted from the equipment client 110 to the server system 100 asevent data. As a result, the processing of the state machine is pushedback on to the equipment client 110 of the machine 128 (e.g., adistributed architecture) rather than centrally at the server system100, allowing for a much more scalable system. In certain embodiments,the transmitted trend data on the first channel 113 a is synchronous tothe transmitted event data on the second channel 113 b, such that anevent identified in the event data is associated with a trend or otherdata from the trend data received by the server system 100 at about thesame time the event data is received. Alternately, the trend data may bereceived separately from the event data. Since the event data isassociated with the trend data, events identified in the event data canbe matched with the associated trend data. The separate transmission oftrend data from event data permits the equipment client processor 114 toidentify the events, making the overall architecture 10 more scalable bybalancing the processing responsibilities for identifying events to theequipment connected to the network 122. Such configuration provides fordramatically increasing the processing capabilities of the trend data inthe system processor 104.

Exemplary equipment clients 110 include heavy duty low profilecomputers, clients, portable computing devices, or suitable devices thathave a low profile (e.g., small in size), are prepared for interferencecaused by being present at a work site, and include an appropriateprocessor 104 and memory 106 capable of executing the instructions andfunctions discussed herein. In certain embodiments, the equipment client110 is wired or wirelessly connected to the network 122 via acommunications module 102 via a modem connection, a local-area network(LAN) connection including the Ethernet, or a broadband wide-areanetwork (WAN) connection, such as a digital subscriber line (DSL),cable, T1, T3, fiber optic, or satellite connection.

The web client 124 is configured to connect to either the server system100 and/or the equipment client 110 over the network 122. This allowsthe web client 124 access to information on the equipment that is storedat either the server system 100. A user of the web client 124 mayprovide information to the server system 100 over network 122, such as,but not limited to, machine capacities, alert criteria, email addresses,annotations, report day offset, etc. In certain embodiments, the webclient 124 accesses the server system 100 using a graphical userinterface provided by the server system 100 and displayed on a display126 of the web client, exemplary screenshots of which are included anddiscussed herein.

As discussed herein, and unless defined otherwise, an alert is anindication of a fault, event, or episode of a machine that may requirehuman attention. Unless otherwise stated, the terms alert and episode,and the terms fault and event, can be used interchangeably. In certainembodiments, an episode is an accumulation of machine events that aremarked in the beginning by the machine shutdown and terminated by themachine 128 having successfully restarted for greater than, for example,30 seconds. The episode is generally identified by the most severe faultthat occurred during this time. In certain embodiments, an event is amachine failure resulting in a shutdown of the machine 128. In certainembodiments, a fault is a predetermined type of event that may indicateabnormal machine operation. In certain embodiments, a machine 128 is apiece of equipment that is being monitored by the server system 100,e.g., shovels, drills, draglines, surface and underground miningmachines, haulage vehicles, mobile mining crushers, or other machinery.In certain embodiments, a trend is a graphical display of machine dataover a set time period.

As discussed above, the system processor 104 of the server system 100 isconfigured to execute instructions for providing a dashboard for theequipment. The dashboard is configured to provide a high levelsituational view of equipment for optimizing maintenance andproductivity goals.

FIG. 2 illustrates an exemplary screenshot of a dashboard 200 asdisplayed on the display 126 of the web client 124. The dashboard 200 isconfigured to provide information regarding a fleet of machines 128monitored by the server system 100 of FIG. 1, such as current fleetstatus, productivity, availability, and utilization. As also illustratedin FIG. 2, the dashboard 200 is configured to provide historicalcomparisons of information and key performance indicators (KPI).

In one embodiment the dashboard 200 includes information on uptimeratios 202, total productivity 204, shovel status 206, total utilization208, load distribution 210, MTBS 212, and main voltage 214. Totalproductivity 204 displays the cycle decomposition for the selectedmachine 128 or an average value if multiple machines 128 have beenselected. Machine status 206 displays the status of all machines 128 ina fleet. Total utilization 208 displays a percentage of utilizationbased on average load and target load (e.g., target dipper load) for aselected machine 128 or an average value if multiple machines 128 havebeen selected. Loading distribution 210 displays the distribution ofloads for a selected machine 128 or an average value if multiplemachines 128 have been selected. MTBS 212 displays the elapsed runningtime between fault-caused shutdowns (not mechanical shutdowns) for aselected machine 128 or an average value if multiple machines 128 havebeen selected. Main voltage 214 displays the average daily counts of lowvoltage (e.g., 5% low) events for a selected machine 128 or an averagevalue if multiple machines 128 have been selected.

Uptime ratios 202 display the machine run time breakdown for a selectedmachine 128 or an average value if multiple machines 128 have beenselected. Uptime ratios 202 provide information (e.g., a pie-chart) onthe uptime 202 of machines in a fleet. In certain embodiments, thesystem 100 calculates the availability of a machine 128 based upon thefollowing equation:

${Availability} = \frac{\sum\limits_{UserDefinedTimePeriod}\left( {{Shutdown\_ time} - {Start\_ time}} \right)}{{Total\_ Time}{\_ User}{\_ Defined}{\_ Time}{\_ Period}}$

This calculation can be displayed as a percentage. The illustrateduptime ratios 202 include the percentage of time that the fleet ofmachines 128 is operational, non-operational, faulted, and when themachines 128 are providing no communication. For example, the uptimeratio can include the time a machine is digging, waiting, propelling, orconducting another activity. In certain embodiments, a machine will, forexample, go into a no communications state when there has been nomessage from the machine for 5 minutes. If communication resumes anddata are received for the no communications period, the nocommunications period is removed and all statistics for the shovel arecorrected.

FIG. 3A is an exemplary state diagram of basic machine states 300 thatare used to determine uptime ratios. Each of the states 300 may beassociated with the categories (e.g., operating, non-operating, faulted,no communication, respectively) of the uptime ratios 202 of FIG. 2according to certain logic, as described in the illustrated table 324.In certain embodiments, each of the categories is assigned a color(e.g., grey, yellow, green, and red).

Following an initial power on state 302, a machine 128 goes into amachine stop operator state 304 (e.g., where equipment is manuallystopped by an operator). From this state 304, the machine 128 enters astart request state 306, from which it then proceeds to either the teststart states 308, 310, 312, 314, or 316, or the started in run state318. Specifically, a machine will, in certain embodiments, transition toone or more of the following states: “Started in Armature Test Mode” 308if started and the Test switch is in the “Armature Test” position;“Started in Control Test Mode” 310 if started and the Test switch is inthe “Control Test” position; “Started in Field Test Mode” 312 if startedand the Test switch is in the “Field Test” position; “Started inAuxiliary Test Mode” 316 if started and the Test switch is in the“Auxiliary Test” position; and, “Started in Run Mode” 318 if started andthe Test switch is in the “Run” position.

From the test states 308, 310, 312, 314, and 316, the machine 128returns to either a machine stop operator state 304, a machine stopinstant state 320, or a machine stop 30-second state 322 (e.g., in bothstates, the machine 128 is automatically stopped). Specifically, incertain embodiments, a machine 128 will transition to “Machine StopOperator Mode” 304 from any state when the operator's cab STOPpushbutton is pressed, a machine 128 will transition to “Machine StopInstant Mode” 320 from any state when an Instant Stop fault isinitiated, and a machine 128 will transition to “Machine Stop 30 secMode” 322 from any state when a 30 second fault is initiated.

From the started in run state 318, the machine continues to the runstates 350 more fully illustrated in FIG. 3B. In one embodiment the runstates 350 include digging 352, motivator mode 354, limits mode 356,cycle decomposition 358, and propel mode 360. The run states 350 proceedto either of the machine stop instant state 320, machine stop 30 secondstate 322, or machine stop operator state 304 discussed above. The logicassociated with each of the run states 350 is described in theassociated table 362 of FIG. 3B.

FIG. 4 is an exemplary screenshot 400 from the web client display 126illustrating a runtime distribution chart for a machine. The chartdetails the various types and amounts (e.g., daily averages 404) ofactivities 402 of each machine 128 in a fleet, and divides thoseactivities 402 into the categories (e.g., operating, non-operating,faulted, no communication) of the uptime ratios 202 of FIG. 2. The chartallows users to view, for example, the amount and the type of abusesthat a machine 128 has been subjected to over a period of time.

FIG. 5 is an exemplary screenshot 500 from the web client display 126displaying productivity information for a machine 128 being monitored bythe system 100 of FIG. 1A. The display includes total productivityinformation 502 and total availability information 504. In certainembodiments, productivity is obtained from the data 118 (e.g., payloaddata) of a machine 128. The display also includes various hour metersand totals for machines 128 in a fleet monitored by the system 100 ofFIG. 1A. The display further includes an abuse factor 510 that displaysan hourly average of abuse-related events (e.g., boom jacks, swingimpacts, motor stalls and low voltage counts) for a selected machine 128or an average value if multiple machines 128 have been selected.

FIG. 6 is an exemplary screenshot 600 from the web client display 126 ofthe load distribution for various machines 128 in a fleet monitored bythe system 100 of FIG. 1A. The load distribution (or “dipper loaddistribution”) is, in certain embodiments, the distribution of totaltruck payloads for a machine 128 over a time period defined by a user.For example, loading distribution can be averaged for all machines 128in a fleet. The illustrated x-axis is the percentage of rated load(e.g., dipper load where 100=rated load), where a user enters the targetrated load for each machine 128. The load distribution includesinformation on overloads 602, allowed loads 604, target loads 606, andloads 15% above a target payload 608. A related loading efficiency canbe calculated as 100 times the average dipper load, or as the measuredpayload divided by the target payload.

FIG. 7 is an exemplary screenshot 700 illustrating information onoutages for a machine 128 being monitored by the system of FIG. 1A. Theinformation includes the top outages by count 744 for a machine 128, thetop outages by downtime 746 for the machine 128, and a filtered outagecause summary grid 742 for the machine. In certain embodiments, theinformation may include the count and downtime related to the mostfrequent faults within a user defined time period.

FIG. 8 illustrates an exemplary screenshot 800 from the web clientdisplay 126 displaying information regarding cycle time performance. Theshovel cycle time graph displays dig cycle time 802 in seconds, swingcycle time 804 in seconds, tuck cycle time 806 in seconds, and swingangle 808 for selected machines 128.

FIG. 9 is an exemplary screenshot 900 illustrating availability historyfor a fleet of machines 128 being monitored by the system 100 of FIG.1A. Specifically, the availability history of six machines 901, 902,903, 904, 905, and 906 is displayed. The availability history for eachof the machines 901, 902, 903, 904, 905, and 906 includes the time eachmachine 901, 902, 903, 904, 905, and 906 was operational 908,non-operational 910, faulted 912, or not in communication 914.

FIG. 10 is an exemplary screenshot 1000 illustrating mean time betweenshutdowns (MTBS) for a fleet of machines 1002, 1004, 1006, 1008, 1010,and 1012 being monitored by the system 100 of FIG. 1A. In the exampleshown, six machines 1002, 1004, 1006, 1008, 1010, and 1012 have a timebetween shutdown value above both the average time between shutdownvalue 1028 of 18, and the target MTBS 1030. The average MTBS is alsorepresented by the first bar 1024. In certain embodiments, thescreenshot may include information on the mean time between shutdown ofeach individual machine 1002, 1004, 1006, 1008, 1010, and 1012 and thetotal mean time between shutdown of all machines 1002, 1004, 1006, 1008,1010, and 1012 of a particular type. In certain embodiments, MTBS isbased on total hours, where the formula is total hours divided by thenumber of episodes in the time period. In certain embodiments, theminimum time period for this calculation is seven days, and if the timeperiod chosen by the user is less than 10 days, the system 100 willforce a 10-day period for the calculation.

Additionally, the system 100 is configured to associate certaininformation trends with faults. For example, certain trends in brakinghistory are related to pneumatic faults, certain trends in lube pumphistory are related to lube flow faults, certain trends in crowd belttension are related to pump faults, certain electrical drive trends arerelated to motor faults, and certain temperature trends are related tothermal faults. FIG. 11 illustrates an exemplary screenshot 1100 fromthe web client display 126 displaying one such trend, specifically, ashort term trend 1102 representing incoming voltage (or power) to ashovel 128. The trend shows the value of the incoming power (in volts)to a machine related to its nominal rating of 100% over a 15-minuteperiod.

FIG. 12 is an exemplary screenshot 1200 illustrating a long term trend1202 representing incoming voltage (or power) to a machine 128.Specifically, the trend 1202 shows the value of the incoming power (involts) to a shovel 128 related to its nominal rating of 100% over a twoweek period. The middle circle 1204 shows an area of the shovel 128where the shovel 128 was likely powered up but idle (e.g., not digging).In that region the voltage regulation of the line is good, and veryclose to 100%. The rightmost circle 1206 shows a three day period inwhich the shovel 128 generally ran well. Of interest is the idle periodat the left side of the circle 1206, and then the increase in variationof the signal as well as the shifting of the mean of the signal toaround 95% as the shovel 128 is in operation. The leftmost circle 1208shows a period in which the regulation is quite poor. There is asignificant magnitude of variations, peaks, lows, and mean, whichindicates that that the shovel 128 is likely to trip in many differentways (e.g., directly from undervoltage, symptoms of the poor regulation,etc.). By identifying these issues with the machine's 128 power, themachine user or owner can, for example, be given prior warning thatcertain actions may cause the machine to begin to trip.

FIG. 13 is an illustration 1300 of an exemplary mobile device displayinginformation 1302 formatted for the mobile device. As part of theworkflow tools provided by the system 100, the system 100 providesinformation to mobile users that allows for automated escalation,instantaneous notifications that are user configurable, and userdefinable events.

FIG. 14 illustrates an exemplary screenshot 1400 from the web clientdisplay 126 displaying workflow tools configured to provide informationand reports directly to users. Specifically, information on alarms, suchas the number 1402 of alarms, types 1404 of alarms, and locations 1406of alarms is displayed. Information on incidents, events, equipment, andescalation can also be provided. The workflow tools are also configuredto provide information on event management, work order generation, andhistorical records.

FIG. 15 is an exemplary screenshot 1500 illustrating in-depth faultanalysis for a fleet of machines 128 being monitored by the system 100of FIG. 1A. The analysis includes trend data on machine volts 1502, amps1504, and revolutions per minute 1504 over time for at least one machine128 monitored by the system 100 of FIG. 1A. The display can be furtherconfigured to display user defined trends, remote analysis, issuediagnosis, and preemptive analysis.

FIG. 16 is an exemplary screenshot 1600 illustrating a historic analysisof temperatures for a fleet of machines 128 being monitored by thesystem 100 of FIG. 1A. The display of FIG. 16 can be further configuredto provide additional information, such as information on motor data,fault data, and sensor data, and it can leverage historical data tosubstantiate predictions. For example, the system 100 includesalgorithms for automatically detecting and identifying failures, such asa failure related to weak crowding. As another example, the system 100can configure the display of FIG. 16 to identify current prognosticindicators, using special odometers (e.g., for drills, cable life, brakelife, and motor life) in order to warn of impending failures (e.g., abearing failure). As another example, the system 100 can configure thedisplay of FIG. 16 to identify measurements that correspond to relevantcondition predictors of motors, such as, but not limited to, motorloading (e.g., over time or in various cycles), drive thermalcalculations (e.g., to give an indication of the historical motorloading), motor energy and work calculations (e.g., calculating theamount of work/energy done by the motor, taking into account any stallconditions, using values such as torque, RMS current, power, RPM, etc.),commutation stress (e.g., the level of commutation stress experienced bythe motor over time and in various cycles) such as rate of currentchange, thermal information (e.g., how heat effects the condition of themotor) such as thermal cycling (e.g., the level of thermal cycling bytracking total changes in temperature over time) and rise above ambient(e.g., measuring total temperature rise over ambient, sensor(s) to beused, interpole, and/or field), and hard shutdowns (e.g., track, bymotion, the number of hard shutdowns, categorized as instant shutdownsor drive trips to the system, and use a weighting system to aid in thequantification of those affects on motor condition).

FIG. 17 is an exemplary screenshot 1700 of a normal trend between twobearings on a hoist drum machine 128 that progressed to a failure,causing an alert to be generated. In certain embodiments, an alert (or“trigger”) is generated based on any combination of (a) a rangeexceeding a predetermined value, (b) if the temperature differencebetween two components is positive (e.g., the difference is normallynegative, so a positive difference may indicate a problem, such as aside stand bearing problem), and (c) if the temperature difference fallsbelow a predetermined negative value (e.g., indicating a problem, suchas a drum gear bearing problem). An alert may be issued in the form of acommunication, such as an email or text message to a user. An alert canhave states, such as open, accepted, resolved, or ignored, and caninclude information such as machine identification, time, associateduser, and status.

Alerts can be configured by a user, as illustrated in the exemplaryscreenshot 1800 of FIG. 18A. Alerts that are configured by a user areconsidered manual alerts. Alerts that are previously configured to becommunicated by the system 100 are considered automatic alerts. Manualalerts can be generated based on a particular fault, or on issues thatmight not be automatically generated by the system 100 (e.g., a crack inthe boom). In certain embodiments, manual alerts can be enabled 1802based on any combination of count 1804, hours 1806, fault code 1808,fault description 1810, severity 1812, and category 1814. Similarly, incertain embodiments, automatic alerts can be issued if the severityweight of an event is above a certain threshold, for example, 800; ifthe code of an event is above a level set by the user for a particularmachine; if an event occurs at or above a user-defined level offrequency within a given timeframe; or, if the calculated value of anMTBS alert level for the past week is less than or equal to the MTBSalert level set by a user. In certain embodiments, duplicate alerts areremoved. For example, if an alert is set to be sent 3 times in 4 hours,after the first alert is sent no other alerts should be sent untilanother 3 in 4 hour pattern is observed in the incoming data. As anotherexample, if several users create an alert definition for a particularmachine and a fault/severity, and if a matching fault/severity occurs,only one alert will be sent. Data regarding the alerts that have beensent by the system 100 can be stored in the system memory 106, andoptionally viewed, as illustrated in FIG. 18B, an exemplary screenshot1850 of a history of alerts communicated by the system 100. FIG. 18Cillustrates an exemplary screenshot 1860 for a predictive model messagealert communication communicated by the system of FIG. 1A. Thepredictive model is discussed in more detail below with reference toFIG. 20B. The exemplary screenshot 1860 illustrates an email received bya user at a web client 124 associated with a machine 128. The emailincludes an identification 1862 of the machine 128, a description of ananomaly 1864 associated with the machine 128, a standard deviation 1866associated with the anomaly 1864, and a date and time at which the alertwas triggered 1868. The email indicates that the mains voltage on themachine 128 is in significant fluctuation with a standard deviation of6.23372.

FIG. 19A illustrates an exemplary screenshot 1900 of a list of faults ofa machine 128 being monitored by the system of FIG. 1A. The faultsdetail sequences of events of record. In one embodiment each listedfault is associated, by column, with a machine 1902 on which the faulttook place, the time 1904 at which the fault took place, a fault code1906 associated with the fault, a description 1908 associated with thefault, a severity weight 1910 associated with the fault, a downtimevalue 1912 associated with the fault, and the subsystem 1914 with whichthe fault relates. The columns may be sorted according to a user'spreferences. For example, the time column can be sorted to show thelatest faults at the top of the listing.

In certain embodiments, the severity weight is based on a weighting thatincludes: (1) a safety value associated with each of the faults, (2) aposition in a logical and/or physical hierarchy associated with each ofthe faults, (3) an estimated time of repair associated with each of thefaults, and (4) an estimated cost of repair associated with each of thefaults. For example, as illustrated in FIG. 19B, which illustratesexemplary weighting determinations 1920 for various faults identified bythe system 100 of FIG. 1, the “Emergency Stop Pushbutton Fault” fault1922 has a severity weight of 769.

In FIG. 19B, the “Emergency Stop Pushbutton Fault” severity weight 1940of 769 is equal to the sum of the safety rank 1924 of 3 times the safetyweight 1932 of 180, the hierarchy rank 1926 of 2 times the hierarchyweight 1934 of 77, the downtime rank 1928 of 1 times the downtime weight1936 of 38, and the cost rank 1930 of 1 times the cost weight 1938 of37. In certain embodiments, the rank values 1924, 1926, and 1928, weightvalues 1932, 1934, and 1936, and weighting determinations 1940 can bedetermined using other methods.

FIG. 19C illustrates an episode of faults 1950 for a machine beingmonitored by the system of FIG. 1A. In certain embodiments, faults aregrouped based on the approximate time at which they occur. For example,if several faults occurred within a 30-second time period, those eventswould be grouped together (as a “group” or “episode”). The fault havingthe highest severity weight from the episode would be considered the“parent” fault, and the remaining faults would be considered the“children” faults. For example, the parent fault (e.g., the most severefault in an episode) is the fault with the highest severity weight thatoccurred within 15 seconds of the first fault in the episode. Withreference to FIG. 19C, the “Hoist Arm contactor aux contact did notclose” 1952 is the parent fault because it has the highest severityweight of 840, while the remaining faults 1954 are its children faultsbecause they have lower severity weight values (not illustrated). Incertain embodiments, the duration for collecting faults for an episodewill be calculated from the first fault to the time a normal startoccurs (e.g., Started In Run condition) or when a no communicationsmessage is issued.

In addition to the reports and exemplary screenshots discussed above,the system 100 is configured to provide reports (with or withoutillustrations) that include information such as, but not limited to,cycle time analysis, average cycle time, tonnage summary, total tonsshipped, average tons per hour, total bench c/yards, average benchc/yards per hour, loading efficiency, and machine hours. The informationmay further include uptime ratio, availability summary, machineavailability breakdown, percentage of availability, mean time betweenshutdown, fault summary, and fault distribution (e.g., the top 5 or 10faults). The information may yet further include the date/time ofrelevant machine faults, recent faults and descriptions, category ofevents, trend of relevant data tags (e.g., as defined by a systemadministrator), link to enter/display annotations, and information onhow to promote an event to an alert. The information may also include alist of most current faults/alarms, trend information with user definedassociations, machine identification, run status, ladder status, andmachine hours (e.g., run, hoist, crowd, swing, propel). The informationmay yet further include average cycle time, current cycle time, currentdipper load, total shipment tonnage, boom jacks, swing impacts, faults,abuse factor, loading efficiency, main voltage level, shovel tons perhour, yards per day, average shovel cycle time, total tons moved, andtotal yards moved. For example, the exemplary report 1970 for a shovel128 illustrated in FIG. 19D provides information related to uptime 1972and outages 1974.

For machines 128 such as drills, the information may also includeaverage hole-to-hole cycle times that is divided up into the individualdrilling process components, the number of holes drilled with an autodrill, manually, or a combination, the total footage drilled and feetdrilled per hour by each drill at a site, with information identified bymachine, over time, so that the data can be compared. The informationmay also include the total number of holes drilled, average number ofholes drilled per day and hour, total footage drilled, average feetdrilled per drilling hour, total drilling hours, average drilling hoursper day, hole-to-hole cycle time, and average cycle time. Theinformation may also include the number and type of exceptionsencountered during machine use, the machine effort, footage, start/endtime, total time to complete a task, total depth, average penetrationrate, total effort, total exception count, pull down, penetration rate,torque, vibration, revolutions per minute, weight on a bit, airpressure, and whether an auto drill was on or off.

In addition to the analyses discussed above, the analysis tools of thesystem 100 are further configured to provide integrated schematics,integrated parts references, annotation history, and RCM enablement.

FIG. 20A illustrates a comparison between an exemplary workflow 2000 ofthe system 100 of FIG. 1A and an exemplary workflow 2050 of a prior artsystem. Specifically, process 2050 illustrates a workflow fortroubleshooting a weak crowd problem according to the prior art, whileprocess 2000 illustrates a workflow troubleshooting a weak crowd problemaccording to certain embodiments of the server system 100.

The prior art process 2050 begins in step 2052, where a machine operatorobserves: (a) a weak crowd problem on a machine 128; (b) that no faultsare present at the time of the complaint; and, (c) that the weak crowdproblem is intermittent. In step 2054, a maintenance technician iscontacted and travels to the site of the machine 128, which takes abouttwo hours. In step 2056, a machine inspection and assessment iscompleted in about one hour. In step 2058, the operator is interviewedand fault logs for the machine 128 are reviewed, taking approximatelyone hour. In step 2060, the maintenance technician installs testequipment and attempts to duplicate the problem, which takes about 8hours. Finally, the problem is identified in step 2062. The entire priorart process 2050 takes, on average, about 12 hours.

The process 2000 as disclosed herein according to certain embodimentssimilarly begins in step 2002, where an machine operator observes: (a) aweak crowd problem on a machine 128; (b) that no faults are present atthe time of the complaint; and, (c) that the weak crowd problem isintermittent. In step 2004, a maintenance technician is contacted, whichtakes about one hour. In step 2006, the maintenance technician logs intothe machine client 110 (in FIG. 1) and begins analyzing any events inthe data 118 (in FIG. 1) associated with the weak crowd problem, takingabout two hours. As illustrated in FIG. 21, the maintenance technicianis able to determine that the amp reading from the data 118 for themachine 128 displays crowd field oscillation (e.g., a proper wave formfollowed by oscillation). The maintenance technician, having analyzedthe data 118, identifies the problem in step 2008. The entire process2000 takes, on average, about 3 hours, or about 25% of the time averagedby the prior art process 2050. The process 2000, having savedsignificant time, allows a problem to be identified before the machine128 fails, which allows for part replacement with minimal downtime(which is related to cost savings) and validation of the machineoperator's awareness in change in performance.

The system 100 for remotely monitoring equipment disclosed hereinadvantageously allows for reductions in Mean Time To Repair (MTTR),unplanned downtime, and operations and maintenance costs. The system 100further allows for improvements in Mean Time Between Failure (MTBF),availability, reliability, maintainability, operating and maintenanceefficiencies, optimization of fleet maintenance and operations,responsiveness to faults, parts and inventory planning, andcompetitiveness and profitability. Productivity, shovel performance, anddata analysis tools are also provided.

As a complement to the process 2000 of FIG. 20A, FIG. 20B illustrates anexemplary workflow 2010 for predicting a machine event using the system100 of FIG. 1A. In certain aspects, the workflow 2010 is referred to asthe “predictive health” of the machine 128.

The workflow 2010 begins in step 2012, where current event data 118(e.g., data within the past few minutes or some other relevant timeperiod) for a machine 128 is received by the server system 100. Incertain aspects, the workflow 2010 determines whether the current eventdata 118 is available before attempting to receive the data. In decisionstep 2014, it is decided whether the data is within operational limits.In certain aspects, the data for the current events is compared with apredetermined physical range for the operation of the machine in orderto determine whether the data is within operation limits. For example,if the data indicates a temperature outside the range of −50 degreesCelsius to 200 degrees Celsius, a range beyond which a physical elementof a machine or component of a machine is unlikely or may even beimpossible to function, then it is likely that the received data iserroneous data. The operational limit that is considered will vary withthe component being analyzed. Thus, if it is decided in decision step2014 that the data 118 is not within operational limits, the data 118 isdiscarded in step 2020 and a data error alert is generated in step 2022to inform a user that the data 118 being received for the machine 128 iserroneous. The user can then, for example, take action to correct thetransmission of the data 118.

If it is decided in decision step 2014 that the data 118 is withinoperational limits, then the workflow 2010 proceeds to step 2016 inwhich it is determined whether the data indicates the existence of ananomaly. The existence of an anomaly can be determined by comparingcurrent events from the data 118 for the machine 128 with past eventsfor the machine 128, or with expected or historical results, todetermine whether an anomaly exists. The existence of an anomaly canalso be determined by comparing current event data 118 for one portionof the machine 128 with current event data 118 for a related portion ofthe machine 128 to determine whether the anomaly exists.

Various anomaly detection techniques can be used for these comparisons.For example, certain techniques include identifying an anomaly usingthresholds and/or statistics. Various statistical considerations includefrequencies, percentiles, means, variances, covariances, and standarddeviations. For example, if the current temperature for a part on themachine 128 is at least one standard deviation away from the averagepast temperature for the machine 128, then an anomaly is identified.Rule-based systems can also be used (e.g., characterizing normal machine128 values using a set of rules and detecting variations therefrom).Another anomaly detection technique is profiling (e.g., buildingprofiles of normal machine 128 behavior and detecting variationstherefrom). Additional anomaly detection techniques include model basedapproaches (e.g., developing a model to characterize normal machine 128data and detecting variations therefrom), and distance based methods(e.g., by computing distances among points).

As another example, if the current event data 118 includes temperatureinformation for at least one portion (e.g., a component) of the machine128, then the temperature of the portion of the machine 128 can becompared to a predetermined temperature range for that portion, or thetemperature of another similar or identical portion of the machine 128to determine whether the anomaly exists for that portion or anotherportion of the machine. Similarly, if the current event data 118includes voltage information, speed information, or count informationfor a portion of the machine 128, then the voltage information, speedinformation, or count information for the portion of the machine 128 canbe compared with a predetermined range of voltage information, speedinformation, and count information for that portion to determine whetheran anomaly exists. Other types of current event data 118 that can becompared with a predetermined range can include electric current data,pressure data, flux data, power data, reference data, time data,acceleration data, and frequency data.

Several examples of models will now be presented that can be used fordetermining whether current event data 118 indicates the existence of ananomaly. A first example relates to the identification of crowd belttensioning. A key criterion to identify a crowd belt on a machine 128being too tight is the temperature on the crowd motor drive end. As thetension in the crowd belt increases, it will hinder the free motion ofthe crowd input end sheave, and an increase in the drive end temperaturewould be expected. In a normal working scenario, both the bearings atthe drive end and non-drive end of the crowd motor are correlated. Dueto a crowd belt being too tight, an external force develops on the crowdmotor input sheave that will create a torque on the armature shaft,resulting in a sharp raise in the drive end bearing temperatures ascompared to the non-drive end bearing temperature. A frequent crowddrive end overheating is a prime indicator of either the crowd beltbeing too tight, or the armature shaft not being aligned properly, whichis a result of a tangential force applied to the one end of the shaft.Thus, in certain embodiments, a model monitors the relative temperaturebetween crowd bearing and a cross-correlation between bearingtemperatures and the crowd belt tension on a predetermined schedule(e.g., every 30 minutes) and identifies an anomaly when bearingtemperatures increase more than three standard distributions.

A second example includes a voltage model. Mines often have powerdistribution issues where the line voltage fluctuates beyond that of themachine specification. During times of voltage dips, excessive voltage,equipment failure or poor power quality, commutation faults can lead toinversion faults. For instance, a machine 128 often has extended (e.g.,more than recommended) trail cable lengths that represent a relativelyhigh impedance seen by the drive system. When one of the drives on themachine turns on its power bridge, the voltage applied to the drives cansuddenly dip or notch. At times, this leads to sudden increase in asilicon-controlled rectifier (SCR) dv/dt rating, which indicates therate of voltage change with respect to time. The model aids in theidentification of conditions that lead to inversion faults. In manycases the model allows for corrective action to take place prior tothese faults occurring. For instance, the disclosed model assists, forexample, with explaining such dips when they are logged. The model alsoassists with diagnosing the root cause of the dip quickly and reliablybased on continuous data collection, thereby helping to understand anypotential short and long term damage to motors and brakes. The modelalso assists with correcting trail cable length, optimizing loaddistribution, and pointing out when voltage regulation has or willbecome an issue with the operation of a machine 128.

A third example includes a DC bus over volts model that detectspremature failing of a machine 128 drives' add-on capacitor modulescaused by repeated DC Bus over voltage events. The model, for example,captures TripRite DC Bus Over Voltage, which is one of the key triggerpoints for drive fault alarms. The model also captures and reports ahigher frequency of drive fault alarms that can prevent prematurefailure of a drives' add-on capacitor modules.

A fourth example includes a crowd belt slippage model. The crowd beltslippage model is configured to detect an instantaneous change in therelative speed between a crowd motor drive end and a first reductionshaft of a crowd gear system. The speed of the first reduction shaft iscalculated from crowd resolver counts, which can be used forcalculations such as crowd torque. Crowd slippage events are effectivelycalculated by monitoring crowd motor speed and crowd resolver counts inorder to identify an anomaly and notify a user when there is a suddendecrease in the resolver counts. Frequent belt slippage is a majorindicator of a crowd belt being too loose. The model facilitatesfrequently monitoring crowd belt slippage and correcting pressure limitsfor auto-tensioning system, and can be used as a precursor for crowdbelt wear out and to estimate downtime for premature crowd belttensioning, thereby potentially avoiding unsafe machine conditions.

A fifth example includes a crowd temperature model. The crowdtemperature model is configured to predict and monitor bearing failuresusing current event data 118. The model accounts for the affects ofambient temperature in a field environment on a machine 128 in assessingwhether to identify an anomaly when bearing temperature reaches acertain temperature, such as 80 degrees Celsius, and suggest shuttingdown the machine 128 when the bearing temperature reaches anotherspecific temperature, such as 90 degrees Celsius. The model facilitatesidentifying crowd bearing failures before they occur, as well asmonitoring the life cycle of a bearing.

Returning to the workflow 2010, if it is decided in decision step 2016that an anomaly does exist (e.g., using the models or anomaly detectiontechniques described above), then an alert comprising information on theanomaly is generated in step 2018. In certain aspects, an anomaly alertis generated if the associated machine data 118 is determined to bedifferentiable (e.g., can be differentiated from other event data forthe same or related machines), anomalous (e.g., is anomalous from otherevent data for the same or related machines), repeatable (e.g., theresults of the data analysis can be repeated), and timely (e.g., aresponse to the anomaly alert can be initiated with sufficient time toaddress the alert). The alert can be transmitted to a user, such as bytelephone call, voice notification, electronic message, text message, orinstant message. The workflow 2010 ends after steps 2018 and 2022.

FIG. 22 is a block diagram illustrating an example of a computer system2200 with which the server system 100 of FIG. 1A can be implemented. Incertain embodiments, the computer system 2200 may be implemented usingsoftware, hardware, or a combination of both, either in a dedicatedserver, or integrated into another entity, or distributed acrossmultiple entities.

Computer system 2200 (e.g., system 100 of FIG. 1) includes a bus 2208 orother communication mechanism for communicating information, and aprocessor 2202 (e.g., processor 104 from FIG. 1) coupled with bus 2208for processing information. By way of example, the computer system 2200may be implemented with one or more processors 2202. Processor 2202 maybe a general-purpose microprocessor, a microcontroller, a Digital SignalProcessor (DSP), an Application Specific Integrated Circuit (ASIC), aField Programmable Gate Array (FPGA), a Programmable Logic Device (PLD),a controller, a state machine, gated logic, discrete hardwarecomponents, or any other suitable entity that can perform calculationsor other manipulations of information. Computer system 2200 alsoincludes a memory 2204 (e.g., memory 106 from FIG. 1), such as a RandomAccess Memory (RAM), a flash memory, a Read Only Memory (ROM), aProgrammable Read-Only Memory (PROM), an Erasable PROM (EPROM),registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any othersuitable storage device, coupled to bus 2208 for storing information andinstructions to be executed by processor 2202. The instructions may beimplemented according to any method well known to those of skill in theart, including, but not limited to, computer languages such asdata-oriented languages (e.g., SQL, dBase), system languages (e.g., C,Objective-C, C++, Assembly), architectural languages (e.g., Java), andapplication languages (e.g., PHP, Ruby, Perl, Python). Instructions mayalso be implemented in computer languages such as array languages,aspect-oriented languages, assembly languages, authoring languages,command line interface languages, compiled languages, concurrentlanguages, curly-bracket languages, dataflow languages, data-structuredlanguages, declarative languages, esoteric languages, extensionlanguages, fourth-generation languages, functional languages,interactive mode languages, interpreted languages, iterative languages,list-based languages, little languages, logic-based languages, machinelanguages, macro languages, metaprogramming languages, multiparadigmlanguages, numerical analysis, non-English-based languages,object-oriented class-based languages, object-oriented prototype-basedlanguages, off-side rule languages, procedural languages, reflectivelanguages, rule-based languages, scripting languages, stack-basedlanguages, synchronous languages, syntax handling languages, visuallanguages, wirth languages, and xml-based languages. Memory 2204 mayalso be used for storing temporary variable or other intermediateinformation during execution of instructions to be executed by processor2202. Computer system 2200 further includes a data storage device 2206,such as a magnetic disk or optical disk, coupled to bus 2208 for storinginformation and instructions.

According to one aspect of the present disclosure, a system for remotelymonitoring machines can be implemented using a computer system 2200 inresponse to processor 2202 executing one or more sequences of one ormore instructions contained in memory 2204. Such instructions may beread into memory 2204 from another machine-readable medium, such as datastorage device 2206. Execution of the sequences of instructionscontained in main memory 2204 causes processor 2202 to perform theprocess steps described herein. One or more processors in amulti-processing arrangement may also be employed to execute thesequences of instructions contained in memory 2204. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions to implement various embodimentsof the present disclosure. Thus, embodiments of the present disclosureare not limited to any specific combination of hardware circuitry andsoftware.

The term “machine-readable medium” as used herein refers to any mediumor media that participates in providing instructions to processor 2202for execution. Such a medium may take many forms, including, but notlimited to, non-volatile media, volatile media, and transmission media.Non-volatile media include, for example, optical or magnetic disks, suchas data storage device 2206. Volatile media include dynamic memory, suchas memory 2204. Transmission media include coaxial cables, copper wire,and fiber optics, including the wires that comprise bus 2208. Commonforms of machine-readable media include, for example, floppy disk, aflexible disk, hard disk, magnetic tape, any other magnetic medium, aCD-ROM, DVD, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, a RAM, a PROM, an EPROM, aFLASH EPROM, any other memory chip or cartridge, a carrier wave, or anyother medium from which a computer can read.

Those of skill in the art would appreciate that the various illustrativeblocks, modules, elements, components, methods, and algorithms describedherein may be implemented as electronic hardware, computer software, orcombinations of both. Furthermore, these may be partitioned differentlythan what is described. To illustrate this interchangeability ofhardware and software, various illustrative blocks, modules, elements,components, methods, and algorithms have been described above generallyin terms of their functionality. Whether such functionality isimplemented as hardware or software depends upon the particularapplication and design constraints imposed on the overall system.Skilled artisans may implement the described functionality in varyingways for each particular application. The use of “including,”“comprising” or “having” and variations thereof herein is meant toencompass the items listed thereafter and equivalents thereof as well asadditional items. The terms “mounted,” “connected” and “coupled” areused broadly and encompass both direct and indirect mounting, connectingand coupling. Further, “connected” and “coupled” are not restricted tophysical or mechanical connections or couplings, and can includeelectrical connections or couplings, whether direct or indirect. Also,electronic communications and notifications may be performed using anyknown means including direct connections, wireless connections, etc.

It is understood that the specific order or hierarchy of steps or blocksin the processes disclosed is an illustration of exemplary approaches.Based upon design preferences, it is understood that the specific orderor hierarchy of steps or blocks in the processes may be rearranged. Theaccompanying method claims present elements of the various steps in asample order, and are not meant to be limited to the specific order orhierarchy presented.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but is to be accorded the full scope consistentwith the language claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. Pronouns in themasculine (e.g., his) include the feminine and neuter gender (e.g., herand its) and vice versa. All structural and functional equivalents tothe elements of the various aspects described throughout this disclosurethat are known or later come to be known to those of ordinary skill inthe art are expressly incorporated herein by reference and are intendedto be encompassed by the claims. Moreover, nothing disclosed herein isintended to be dedicated to the public regardless of whether suchdisclosure is explicitly recited in the claims. No claim element is tobe construed under the provisions of 35 U.S.C. § 112, sixth paragraph,unless the element is expressly recited using the phrase “means for” or,in the case of a method claim, the element is recited using the phrase“step for.”

While certain aspects and embodiments of the invention have beendescribed, these have been presented by way of example only, and are notintended to limit the scope of the invention. Indeed, the novel methodsand systems described herein may be embodied in a variety of other formswithout departing from the spirit thereof. The accompanying claims andtheir equivalents are intended to cover such forms or modifications aswould fall within the scope and spirit of the invention.

1. A method of identifying bearing failures of crowd bearings of a crowdsystem of a mining machine with a processor that accounts for effects ofambient temperature, the method comprising: receiving, by the processor,crowd data indicating bearing temperatures of the crowd bearings sensedby at least one sensor arranged to sense the bearing temperatures;determining, by the processor when the bearing temperatures reach acertain temperature to assess whether to identify an anomaly of bearingfailure taking into account the effects of ambient temperature;generating an alert indicating the anomaly of bearing failure based onthe bearing temperatures reaching the certain temperature and theeffects of the ambient temperature; determining, with the processor,whether the bearing temperatures reach another specific temperature thatis greater than the certain temperature; and shutting down operation ofthe mining machine based on the bearing temperatures reaching theanother specific temperature.
 2. The method according to claim 1,wherein the certain temperature is about 80 degrees Celsius.
 3. Themethod according to claim 2, wherein the another specific temperature isabout 90 degrees Celsius.
 4. The method according to claim 1, furthercomprising transmitting the alert to a user, wherein the anomaly ofbearing failure is for one or more of the crowd bearings.
 5. The methodaccording to claim 4, wherein the alert is transmitted by at least oneof a telephone call, voice notification, electronic message, textmessage, or instant message.
 6. A system for identifying a failure ofcrowd bearings of a crowd system of a mining machine with a processorthat accounts for effects of ambient temperature, the system comprising:sensors for obtaining bearing temperatures of crowd bearings; and aprocessor for receiving the bearing temperatures of the crowd bearings,the processor configured to: determine when the bearing temperaturesreach a certain temperature to assess whether to identify an anomaly ofthe failure of the crowd bearings taking into account the effects ofambient temperature; generate an alert indicating the anomaly based onthe failure of the crowd bearings; determine whether the bearingtemperatures reach another specific temperature that is greater than thecertain temperature; and shut down operation of the mining machine basedon the bearing temperatures reaching the another specific temperature.7. The system according to claim 6, wherein the certain temperature isabout 80 degrees Celsius.
 8. The system according to claim 7, whereinthe another specific temperature is about 90 degrees Celsius.
 9. Thesystem according to claim 7, wherein the processor is configured totransmit the alert to a user, and wherein the anomaly of bearing failureis for one or more of the crowd bearings.
 10. The system according toclaim 9, wherein the alert is transmitted by at least one of a telephonecall, voice notification, electronic message, text message, or instantmessage.